Summary of Exploring Social Desirability Response Bias in Large Language Models: Evidence From Gpt-4 Simulations, by Sanguk Lee et al.
Exploring Social Desirability Response Bias in Large Language Models: Evidence from GPT-4 Simulations
by Sanguk Lee, Kai-Qi Yang, Tai-Quan Peng, Ruth Heo, Hui Liu
First submitted to arxiv on: 20 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel investigation employs GPT-4 to simulate human-like responses in social surveys, exploring whether large language models (LLMs) develop biases akin to the social desirability response (SDR) bias. To this end, GPT-4 was assigned personas from four societies, utilizing data from the 2022 Gallup World Poll. The results indicate mixed effects: while a commitment statement increased SDR index scores, suggesting SDR bias, it reduced civic engagement scores, revealing an opposite trend. Notably, demographic associations were found with SDR scores and the commitment statement had limited impact on GPT-4’s predictive performance. This study highlights potential avenues for using LLMs to investigate biases in both humans and LLMs themselves. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are trying to help us understand how people answer social surveys. They might even develop their own biases, just like we do! To find out, scientists used a special model called GPT-4 and asked it questions with or without a promise statement that makes people feel more honest. The results were interesting: when they promised honesty, the answers got better but also became less engaged in civic issues. They found some patterns between demographics and how honest people are willing to be. This study is important because it shows us how we can use these language models to learn more about biases in ourselves and computers. |
Keywords
» Artificial intelligence » Gpt
